cross-market-pricer
# Cross-Market Pricer
Normalize and compare odds across Polymarket, Kalshi, and traditional sportsbooks.
## When to Use
Use this skill when the user asks about:
- Comparing odds across different platforms (sportsbooks vs prediction markets)
- Normalizing prices to a common format
- Side-by-side pricing for the same event on Polymarket, Kalshi, and a sportsbook
- Whether there's a pricing gap or mispricing between platforms
- Converting between American odds, implied probability, and contract prices
## Conversion Reference
American to probability: negative → |odds|/(|odds|+100), positive → 100/(odds+100)
Polymarket to probability: price is already probability
Kalshi to probability: contract_price (already 0.00–1.00 from API)
Probability to American: >0.5 → -(prob/(1-prob))*100, <0.5 → +((1-prob)/prob)*100
## Operations
### 1. Fetch Sportsbook Odds as Normalized Probability
Pull American odds from The Odds API and convert to implied probability inline:
```bash
curl -s "https://api.the-odds-api.com/v4/sports/SPORT_KEY/odds?apiKey=$ODDS_API_KEY®ions=us&markets=h2h&oddsFormat=american" \
| jq '[.[] | {
event: "\(.away_team) vs \(.home_team)",
start: .commence_time,
source: "sportsbooks",
outcomes: [.bookmakers[0].markets[0].outcomes[] | {
name: .name,
american_odds: .price,
implied_prob: (if .price < 0 then (-((.price)) / (-((.price)) + 100)) else (100 / (.price + 100)) end) | (. * 1000 | round / 1000)
}]
}]'
```
Replace SPORT_KEY with the appropriate key (e.g., basketball_nba, americanfootball_nfl).
### 2. Fetch Polymarket Prices as Normalized Probability
Pull market prices from Polymarket's Gamma API:
```bash
curl -s "https://gamma-api.polymarket.com/markets?closed=false&limit=10&tag=CATEGORY" \
| jq '[.[] | {
event: .question,
source: "polymarket",
outcomes: [{
name: "Yes",
polymarket_price: (.outcomePrices | fromjson | .[0] | tonumber | (. * 1000 | round / 1000)),
implied_prob: (.outcomePrices | fromjson | .[0] | tonumber | (. * 1000 | round / 1000))
}, {
name: "No",
polymarket_price: (.outcomePrices | fromjson | .[1] | tonumber | (. * 1000 | round / 1000)),
implied_prob: (.outcomePrices | fromjson | .[1] | tonumber | (. * 1000 | round / 1000))
}],
volume: .volume,
liquidity: .liquidity
}]'
```
Replace CATEGORY with the relevant tag (e.g., "sports", "nba", "politics", "crypto").
### 3. Fetch Kalshi Contract Prices as Normalized Probability
Pull contract prices from Kalshi's public market data API:
```bash
curl -s "https://api.elections.kalshi.com/trade-api/v2/markets?status=open&limit=10&series_ticker=SERIES_TICKER" \
| jq '[.markets[] | {
event: .title,
source: "kalshi",
ticker: .ticker,
outcomes: [{
name: "Yes",
kalshi_price: (.yes_ask / 100),
implied_prob: (.yes_ask / 100)
}, {
name: "No",
kalshi_price: (.no_ask / 100),
implied_prob: (.no_ask / 100)
}],
volume: .volume
}]'
```
Replace SERIES_TICKER with the Kalshi series ticker for the event category.
### 4. Unified Cross-Market Comparison
When the user wants to compare the SAME event across platforms, fetch from each source and use Python to produce a unified table:
```bash
python3 -c "
import json, subprocess
def american_to_prob(odds):
if odds < 0:
return round(abs(odds) / (abs(odds) + 100), 4)
else:
return round(100 / (odds + 100), 4)
def prob_to_american(prob):
if prob >= 1: return -99999
if prob <= 0: return 99999
if prob > 0.5:
return round(-(prob / (1 - prob)) * 100)
elif prob < 0.5:
return round(((1 - prob) / prob) * 100)
else:
return 100
# Input: user provides these values (agent fills from context)
platforms = {
'PLATFORM_1_NAME': {'yes_prob': PLATFORM_1_YES_PROB, 'no_prob': PLATFORM_1_NO_PROB},
'PLATFORM_2_NAME': {'yes_prob': PLATFORM_2_YES_PROB, 'no_prob': PLATFORM_2_NO_PROB},
}
print('=== Cross-Market Comparison ===')
print(f\"{'Platform':<18} {'Yes Prob':>10} {'Yes Amer':>10} {'No Prob':>10} {'No Amer':>10} {'Total':>8}\")
print('-' * 70)
for name, data in platforms.items():
y = data['yes_prob']
n = data['no_prob']
total = round(y + n, 4)
y_am = prob_to_american(y)
n_am = prob_to_american(n)
y_am_s = f'+{y_am}' if y_am > 0 else str(y_am)
n_am_s = f'+{n_am}' if n_am > 0 else str(n_am)
print(f'{name:<18} {y:>10.1%} {y_am_s:>10} {n:>10.1%} {n_am_s:>10} {total:>8.1%}')
# Flag mispricings
probs = [d['yes_prob'] for d in platforms.values()]
gap = max(probs) - min(probs)
if gap > 0.03:
print(f'\n⚠ MISPRICING: {gap:.1%} gap across platforms on Yes side')
else:
print(f'\nPricing aligned within {gap:.1%} — no actionable gap')
"
```
The agent must fill in PLATFORM_1_NAME, PLATFORM_1_YES_PROB, etc. from the results of Operations 1–3. If an event only exists on two platforms, remove the third entry.
### 5. Quick Single-Value Conversion
For ad-hoc conversions when the user gives a single odds value:
```bash
python3 -c "
odds_input = 'INPUT_VALUE'
# Detect format and normalize
if odds_input.startswith('+') or odds_input.startswith('-'):
odds = int(odds_input)
prob = abs(odds)/(abs(odds)+100) if odds < 0 else 100/(odds+100)
fmt = 'American'
elif '.' in odds_input and float(odds_input) <= 1:
prob = float(odds_input)
fmt = 'Probability'
elif odds_input.startswith('\$'):
prob = float(odds_input.replace('\$',''))
fmt = 'Kalshi contract'
else:
prob = float(odds_input)
fmt = 'Decimal probability' if float(odds_input) <= 1 else 'Unknown'
# Convert to all formats
if prob > 0.5:
american = round(-(prob/(1-prob))*100)
elif prob < 0.5:
american = round(((1-prob)/prob)*100)
else:
american = 100
am_str = f'+{american}' if american > 0 else str(american)
print(f'Input: {odds_input} ({fmt})')
print(f'Implied probability: {prob:.1%}')
print(f'American odds: {am_str}')
print(f'Kalshi contract: \${prob:.2f}')
print(f'Polymarket share: {prob:.4f}')
"
```
Replace INPUT_VALUE with the user's value (e.g., "-150", "0.62", "$0.58").
## Output Rules
1. Always show implied probability as the primary format (3 decimal places, e.g., 0.620)
2. Include American odds equivalent alongside probability for sportsbook-familiar users
3. When comparing platforms, display as a table with columns: Platform, Yes Prob, Yes American, No Prob, No American, Total
4. The "Total" column (Yes + No probabilities) reveals vig — anything over 100% is overround
5. Flag gaps greater than 3% between platforms as potential mispricings
6. Always note which sportsbook was used as the sportsbook source (since books differ)
7. Report The Odds API quota after any sportsbook fetch
## Error Handling
- If ODDS_API_KEY is not set, tell the user to get a free key at https://the-odds-api.com/
- If Polymarket returns empty results for a category, try broader search terms or list available categories
- If Kalshi returns empty results, the event may not have an active contract — suggest checking kalshi.com directly
- If an event exists on one platform but not another, report what's available and note the gap
- If rate limited on any API, report the limitation and suggest which platforms can still be queried
## About
Built by [AgentBets](https://agentbets.ai) — full tutorial at [agentbets.ai/guides/openclaw-cross-market-pricer-skill/](https://agentbets.ai/guides/openclaw-cross-market-pricer-skill/).
Part of the [OpenClaw Skills series](https://agentbets.ai/guides/#openclaw-skills) for the [Agent Betting Stack](https://agentbets.ai/guides/agent-betting-stack/).
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